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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- common_voice_17_0 |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xlsr-53-sw-tokenizer |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: common_voice_17_0 |
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type: common_voice_17_0 |
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config: sw |
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split: test |
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args: sw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.3240375929734197 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-large-xlsr-53-sw-tokenizer |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_17_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4306 |
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- Wer: 0.3240 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 1000 |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:-----:|:---------------:|:------:| |
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| No log | 0.1721 | 1000 | 0.7966 | 0.7685 | |
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| No log | 0.3441 | 2000 | 0.5178 | 0.5562 | |
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| 2.0511 | 0.5162 | 3000 | 0.4524 | 0.5039 | |
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| 2.0511 | 0.6882 | 4000 | 0.4207 | 0.4615 | |
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| 2.0511 | 0.8603 | 5000 | 0.4031 | 0.4437 | |
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| 0.2699 | 1.0323 | 6000 | 0.3875 | 0.4224 | |
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| 0.2699 | 1.2044 | 7000 | 0.3870 | 0.4141 | |
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| 0.2699 | 1.3765 | 8000 | 0.3811 | 0.4143 | |
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| 0.1994 | 1.5485 | 9000 | 0.3689 | 0.4026 | |
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| 0.1994 | 1.7206 | 10000 | 0.3603 | 0.3915 | |
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| 0.1994 | 1.8926 | 11000 | 0.3561 | 0.3862 | |
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| 0.1838 | 2.0647 | 12000 | 0.3502 | 0.3809 | |
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| 0.1838 | 2.2368 | 13000 | 0.3580 | 0.3763 | |
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| 0.1838 | 2.4088 | 14000 | 0.3445 | 0.3747 | |
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| 0.1472 | 2.5809 | 15000 | 0.3416 | 0.3720 | |
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| 0.1472 | 2.7529 | 16000 | 0.3599 | 0.3709 | |
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| 0.1472 | 2.9250 | 17000 | 0.3503 | 0.3666 | |
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| 0.1405 | 3.0970 | 18000 | 0.3549 | 0.3624 | |
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| 0.1405 | 3.2691 | 19000 | 0.3476 | 0.3582 | |
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| 0.1405 | 3.4412 | 20000 | 0.3359 | 0.3574 | |
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| 0.116 | 3.6132 | 21000 | 0.3487 | 0.3600 | |
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| 0.116 | 3.7853 | 22000 | 0.3439 | 0.3552 | |
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| 0.116 | 3.9573 | 23000 | 0.3502 | 0.3579 | |
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| 0.1103 | 4.1294 | 24000 | 0.3436 | 0.3513 | |
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| 0.1103 | 4.3014 | 25000 | 0.3502 | 0.3502 | |
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| 0.1103 | 4.4735 | 26000 | 0.3381 | 0.3534 | |
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| 0.0957 | 4.6456 | 27000 | 0.3411 | 0.3482 | |
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| 0.0957 | 4.8176 | 28000 | 0.3425 | 0.3456 | |
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| 0.0957 | 4.9897 | 29000 | 0.3331 | 0.3425 | |
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| 0.0883 | 5.1617 | 30000 | 0.3620 | 0.3449 | |
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| 0.0883 | 5.3338 | 31000 | 0.3403 | 0.3430 | |
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| 0.0883 | 5.5058 | 32000 | 0.3590 | 0.3429 | |
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| 0.0757 | 5.6779 | 33000 | 0.3474 | 0.3402 | |
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| 0.0757 | 5.8500 | 34000 | 0.3395 | 0.3378 | |
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| 0.0757 | 6.0220 | 35000 | 0.3565 | 0.3395 | |
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| 0.0695 | 6.1941 | 36000 | 0.3729 | 0.3397 | |
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| 0.0695 | 6.3661 | 37000 | 0.3676 | 0.3368 | |
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| 0.0695 | 6.5382 | 38000 | 0.3748 | 0.3364 | |
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| 0.0601 | 6.7103 | 39000 | 0.3783 | 0.3360 | |
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| 0.0601 | 6.8823 | 40000 | 0.3657 | 0.3363 | |
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| 0.0601 | 7.0544 | 41000 | 0.3808 | 0.3343 | |
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| 0.0542 | 7.2264 | 42000 | 0.3934 | 0.3361 | |
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| 0.0542 | 7.3985 | 43000 | 0.3787 | 0.3369 | |
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| 0.0542 | 7.5705 | 44000 | 0.3920 | 0.3310 | |
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| 0.0487 | 7.7426 | 45000 | 0.3906 | 0.3321 | |
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| 0.0487 | 7.9147 | 46000 | 0.3934 | 0.3323 | |
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| 0.0487 | 8.0867 | 47000 | 0.4060 | 0.3305 | |
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| 0.0412 | 8.2588 | 48000 | 0.4145 | 0.3301 | |
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| 0.0412 | 8.4308 | 49000 | 0.4125 | 0.3282 | |
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| 0.0412 | 8.6029 | 50000 | 0.4111 | 0.3286 | |
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| 0.0381 | 8.7749 | 51000 | 0.4113 | 0.3265 | |
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| 0.0381 | 8.9470 | 52000 | 0.4147 | 0.3268 | |
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| 0.0381 | 9.1191 | 53000 | 0.4221 | 0.3271 | |
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| 0.0338 | 9.2911 | 54000 | 0.4299 | 0.3268 | |
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| 0.0338 | 9.4632 | 55000 | 0.4221 | 0.3250 | |
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| 0.0338 | 9.6352 | 56000 | 0.4314 | 0.3245 | |
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| 0.0318 | 9.8073 | 57000 | 0.4307 | 0.3243 | |
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| 0.0318 | 9.9794 | 58000 | 0.4306 | 0.3240 | |
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### Framework versions |
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- Transformers 4.55.4 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 3.6.0 |
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- Tokenizers 0.21.4 |
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